A Forward Kinematics Data Structure for Efficient Evolutionary Inverse Kinematics
Various approaches to solving inverse kinematics implicitly rely on computing forward kinematics in order to obtain an approximate solution. This work proposes an optimised data structure to efficiently compute these equations by avoiding redundant transformations and calculations. This is particulary relevant for highly articulated kinematic models and multiple end effectors with shared joints along their kinematic chains. By integrating the developed OFKT (Optimised Forward Kinematics Tree), less computation time within each iteration is required, which contributes to a significant speedup in convergence. Experiments were conducted using a novel evolutionary approach which was designed for handling complex kinematic geometries.
KeywordsForward kinematics Inverse kinematics Data structures Computational efficiency Evolutionary optimisation Robotics Character animation
This research was funded by the German Research Foundation (DFG) and the National Science Foundation of China (NSFC) in project Crossmodal Learning, TRR-169.
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